A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
Open accessAbstract
BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission. METHODS: We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system. RESULTS: Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital. CONCLUSIONS: Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.
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Missing data were usually ignored by dropping data or imputing, and if not dealt with appropriately can skew the results.
“Missing data were usually ignored by dropping data or imputing, if not dealt with appropriately can skew the results68,106,173.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [106]
The Methods section (Feature vector generation and model building; page not available) says the authors tested several ways to fill missing values and treated missing values as zero. The Discussion (Strengths, limitations, and challenges; page not available) calls missing EMR data a limitation and says greater data consistency may affect model performance. The paper does not say missing data are usually ignored or handled by dropping data, nor does it establish that inappropriate handling skews results. (LLM, conf=0.83)
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- Authors
- Golas, S. B., Shibahara, T., Agboola, S., Otaki, H., Sato, J., Nakae, T., Hisamitsu, T., Kojima, G., Felsted, J., Kakarmath, S., Kvedar, J., & Jethwani, K.
- Affiliations
- Partners HealthCare Connected Health (US)
- Hitachi (Japan) (JP)
- Harvard University (US)
- Corresponding author
- Golas, Sara Bersche
- ORCID
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2018-06-22
- Volume
- 18
- Issue
- 1
- Pages
- 44-44
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC6013959
- OpenAlex
- W2808897169
- Cited by
- 287
- References
- 42
- Citations by year
- 2026: 17 · 2025: 39 · 2024: 37 · 2023: 37 · 2022: 40 · 2021: 59 · 2020: 35 · 2019: 21 · 2018: 2
- FWCI
- 16.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Central Research Laboratory, Hitachi, Ltd.
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Keywords
- heart failure, 30-day readmission, readmission prediction, electronic health records, clinical notes, hospital readmission, healthcare cost reduction, disease management programs, deep learning prediction
- MeSH terms
- Deep Learning, Aged, Aged, 80 and over, Female, Heart Failure, Humans, Male, Middle Aged, Models, Theoretical, Patient Readmission, Prognosis, Retrospective Studies, Electronic Health Records
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